Exploring the Drivers for the Adoption of Metaverse Technology in Engineering Education using PLS-SEM and ANFIS

Abstract

The rapid development of metaverse technology provides countless opportunities for social interaction, collaboration, communication, and knowledge-sharing that will significantly impact human life. To ensure widespread adoption and acceptance, however, issues concerning approval, accessibility, privacy, and user behavior must be resolved. Therefore, this study investigated the drivers of metaverse technology adoption for engineering education by utilizing an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model that incorporates variables such as hedonic motivation, habit, trust in technology, and cyber security. The study collected data from 370 respondents and then carried out an analysis of the data using partial least squares structural equation modeling (PLS-SEM) and an adaptive neuro-fuzzy inference system (ANFIS). The findings indicated that cyber security, performance expectancy, social influence, and hedonic motivation have a significant impact on behavior intention to use metaverse technology for learning, with cyber security having the strongest effect. These results provide important insights for organizations seeking to enhance their cyber security practices and promote positive user behavior. Additionally, the study highlighted ways to improve the adoption and acceptance of metaverse technology in engineering education. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.